Kavya Goyal·Sep 7The cost of Finding Out 👀You can’t learn without spending, and you can’t spend well without learning.
InData And BeyondbyDima Iakubovskyi·Sep 1Multiarmed Bandits 101: Earn More And Measure WorseAn adaptive policy saved 75 of the 100 conversions my A/B test threw away, then overstated the winning margin by 38 percent
Sridatt More·Jul 29Making RL agent complex is not the same as making it smart!Using Contextual bandits to solve the Tragedy of commons.
Adedayoadejobi·Jul 9When the State Cannot Protect, Should Citizens Defend Themselves?As kidnappers, terrorists, and bandits spread fear across Nigeria, millions increasingly question whether a government unable to guarantee…
Ec·Jul 9Applying Bandit SAST to VulnBank: Static Security Analysis of a Vulnerable Flask ApplicationIntroduction
Sujangyawali·Jun 27Reinforcement Learning — BasicsReinforcement Learning (RL) is a machine learning approach where an agent learns through trial and error by interacting with an…
Atharva Aglawe·Jun 15DevRouter: Teaching AI Coding Agents to Actually Remember Your CodebaseAI coding agents spend most of their tokens rediscovering context they already learned yesterday. DevRouter turns your repository into a…
Sridatt More·Jun 9How would classic RL algorithms react in a dynamic world?Bandit Algorithms in Bioeconomics: Optimising Policy Levers in a State-Dependent System using MAB.
Abdelfatah Kermali·Mar 29The Multi-Armed Bandit Problem: Your First Real RL AlgorithmImagine you run a news website.
Promit Haldar·Mar 21Multi-Armed Bandits, Explained: The Epsilon-Greedy Algorithm from First PrinciplesA Deep Dive into Multi-Armed Bandit Testing
